The effect of a cause has been quantified using information theory [1]}, however, without considering learning in a Bayesian setting. Entropic causal inference (see [2]}) specifies circumstances where the causal direction between categorical variables can be determined from observational data under assumptions of limited entropy. Information geometry has been proposed as a means to infer causal orientation by relying on distributional assumptions [3]}, however both settings are different from the Bayesian learning setting considered here. Targeted intervention selection has also been considered by [4]}, [5]} but using a different measure to quantify the goodness of an intervention.
